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Saltz  \nSyracuse University  \nJeffrey M. Stanton  \nSyracuse University  \nFOR INFORMATION: SAGE Publications, Inc.  \n2455 Teller Road  \nThousand Oaks, California 91320  \nE-mail: [order@sagepub.com](order@sagepub.com)  \nSAGE Publications Ltd.  \n1 Oliver ’s Yard  \n55 City Road  \nLondon EC1Y 1SP United Kingdom  \nSAGE Publications India Pvt. Ltd.  \nB 1/I 1 Mohan Cooperative Industrial Area Mathura Road, New Delhi 110 044 India  \nSAGE Publications Asia-Pacific Pte. Ltd.  \n3 Church Street  \n\\#10-04 Samsung Hub Singapore 049483  \nCopyright © 2018 by SAGE Publications, Inc. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.  \nAll trademarks depicted within this book, including trademarks appearing as part of a screenshot, figure, or other image are included solely for the purpose of illustration and are the property of their respective holders. The use of the trademarks in no way indicates any relationship with, or endorsement by, the holders of said trademarks. RStudio and Shiny are trademarks of RStudio, Inc.  \nThe R Foundation owns the copyright to R software and licenses it under the GNU General Public License 2.0, [https://www.r-project.org/COPYING](https://www.r-project.org/COPYING). The R content depicted in this book is included solely for purposes of illustration and is owned by The R Foundation and in no way indicates any relationship with, or endorsement by The R Foundation. The R software  \nlogo as it appears in this book is available at [https://www.r-project.org/logo/](https://www.r-project.org/logo/)[ ](https://www.r-project.org/logo/)and is copyright protected by The R Foundation and licensed under Creative Commons Attribution-ShareAlike 4.0 International license (CCBY-SA 4.0) [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/) .  \nPrinted in the United States of America  \nLibrary of Congress Cataloging-in-Publication Data  \nNames: Saltz, Jeffrey S., author. | Stanton, Jeffrey M., 1961-author.  \nTitle: An introduction to data science / Jeffrey S. Saltz—Syracuse University, Jeffrey M. Stanton—Syracuse University, USA.  \nDescription: First edition. | Los Angeles : SAGE,[2018] | Includes bibliographical references and index.  \nIdentifiers: LCCN 2017011487 | ISBN 9781506377537 (pbk. : alk. paper)  \nSubjects: LCSH: Databases. | R (Computer program language)  \nClassification: LCC QA76.9.D32 S38 2018 | DDC 005.74—dc23 LC record  \navailable at [https://lccn.loc.gov/2017011487](https://lccn.loc.gov/2017011487)[ ](https://lccn.loc.gov/2017011487)[This book is printed on acid-free paper](This book is printed on acid-free paper).  \nAcquisitions Editor: Leah Fargotstein  \nContent Development Editor: Laura Kirkhuff  \nProduction Editor: Kelly DeRosa  \nCopy Editor: Alison Hope  \nTypesetter: C&M Digitals (P) Ltd.  \nProofreader: Wendy Jo Dymond  \nIndexer: Sheila Bodell  \nCover Designer: Michael Dubowe  \nMarketing Manager: Susannah Goldes  \nContents  \nPreface  \nAbout the Authors  \nIntroduction: Data Science, Many Skills  \nWhat Is Data Science?  \nThe Steps in Doing Data Science  \nThe Skills Needed to Do Data Science  \nChapter 1 • About Data  \nStoring Data—Using Bits and Bytes  \nCombining Bytes Into Larger Structures  \nCreating a Data Set in R  \nChapter 2 • Identifying Data Problems  \nTalking to Subject Matter Experts  \nLooking for the Exception  \nExploring Risk and Uncertainty  \nChapter 3 • Getting Started With R  \nInstalling R  \nUsing R  \nCreating and Using Vectors  \nChapter 4 • Follow the Data  \nUnderstand Existing Data Sources  \nExploring Data Models  \nChapter 5 • Rows and Columns  \nCreating Dataframes  \nExploring Dataframes  \nAccessing Columns in a Dataframe  \nChapter 6 • Data Munging  \nReading a CSV Te","cbCaip8L4OgJEPaX","https://ap.wps.com/l/cbCaip8L4OgJEPaX","pdf",14381792,296,"English","# Preface\n# About the Authors\n# Introduction: Data Science, Many Skills\n## What Is Data Science?\n## The Steps in Doing Data Science\n## The Skills Needed to Do Data Science\n# Chapter 1 • About Data\n## Storing Data—Using Bits and Bytes\n## Creating a Data Set in R\n# Chapter 2 • Identifying Data Problems\n## Talking to Subject Matter Experts\n## Exploring Risk and Uncertainty\n# Chapter 3 • Getting Started With R\n## Installing R\n## Creating and Using Vectors\n# Chapter 4 • Follow the Data\n## Understand Existing Data Sources\n## Exploring Data Models\n# Chapter 5 • Rows and Columns\n## Creating Dataframes\n## Accessing Columns in a Dataframe\n# Chapter 6 • Data Munging\n## Reading a CSV Text File\n## Cleaning Up the Elements\n# Chapter 7 • Onward With RStudio®\n## Installing RStudio\n## Creating R Scripts\n# Chapter 8 • What’s My Function?\n## Creating Functions in R\n## Testing Functions\n# Chapter 9 • Beer, Farms, and Peas and the Use of Statistics\n## Sampling a Population\n## Using Descriptive Statistics\n# Chapter 10 • Sample in a Jar\n## Sampling in R\n## Central Limit Theorem","[{\"question\":\"What does the book teach at the start of the data science workflow?\",\"answer\":\"It introduces what data science is, the steps involved in doing data science, and the skills needed to carry them out.\"},{\"question\":\"How does the book build R proficiency for working with data?\",\"answer\":\"It covers installing and using R, creating vectors, building and exploring dataframes, and performing data munging tasks like reading CSV files and cleaning elements.\"},{\"question\":\"Which advanced data science topics are covered beyond basic data handling?\",\"answer\":\"The later chapters address statistics and sampling, storage and importing data, visualization with ggplot2, text mining and sentiment analysis, modeling, data mining with association rules, and building Shiny web apps.\"}]","An Introduction to Data Science - First edition | PDF",746]